Stephen M. Chu

ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

Matrix approximation (MA) is one of the most popular techniques in today's recommender systems. In most MA-based recommender systems, the problem of risk minimization should be defined, and how to achieve minimum expected risk in …

Collaborative Filtering with Noisy Ratings

2019 · Society for Industrial and Applied Mathematics eBooks

User ratings on items are noisy in real-world recommender systems, which raises challenges to matrix approximation (MA)-based collaborative filtering (CF) algorithms — the learned models will be easily biased to the noisy training data and …

Transferable AutoML by Model Sharing Over Grouped Datasets

2019

Automated Machine Learning (AutoML) is an active area on the design of deep neural networks for specific tasks and datasets. Given the complexity of discovering new network designs, methods for speeding up the search procedure …